The social services sector in Victoria is a multi billion dollar sector providing a range of services with the common focus of reducing poverty and disadvantage experienced by Victorians. The sector employs thousands of Victorians, making a substantial contribution to the state economy. There are a large number of charities and not-for-profit organisations that operate within the social services sector in Victoria, often deriving funding from very different sources than that of their privately-run counterparts.
As the social services sector in Victoria is so large and complex, this report dives into the data to better understand its composition and characteristics. This report and its data visualisations make use of data provided by the Australian Charities and Not-for-profits Commission (ACNC) to examine the impact of charities and not-for-profit organisations in the social services sector in Victoria. This report also looks at changes in the nature of the charities and not-for-profits in the social services sector between 2016 and 2017.
The ACNC Data for 2016 was previously accessed and cleaned by VCOSS and provided for analysis. The 2017 ACNC data was sourced and cleaned for this project to match the 2016 data. The ACNC data is self-reported information provided to the ACNC by charities. Please see Appendix 1a for more information on how the data was prepared to VCOSS standards.
The ACNC gave some charities the choice to report their information as a group or a block. These were included in the original data source and were linked to invalid ABN codes. Entries with invalid ABN codes were removed from the data for both 2016 and 2017. For more information on how invalid ABNs can be identified, please see the ABN website. For details on how these criteria were used to identify and remove invalid ABNs from the datasets, please see Appendix 1b. To better understand the structure of the data, click on the tabs below to see truncated excerpts of each year’s dataset.
The below visualisations are all fully interactive; allowing you to focus and reset all of the elements that comprise each plot. Let your curiosity flow; click on legends to remove all other entries, double click legends to remove that individual entry, click and hold to zoom, use sliders to change year, reset and autoscale axes using the fading menu towards the top right.
Charities operating in the social services sector comprise more than one out of every five charities operating in Victoria. In 2016 this was 1,062 charities, which decreased slighty in 2017 to 1,022 (22.8% and 22.0% of total charities, respectively).
In 2016, the largest number of charities were working in the Social Services sector with Other Education recording the second-most number of charities at 1,062. These figures changed in 2017, with Other Education recording the greatest number of charities at 1,136 while Social Services decreased slightly at 1,022 charities.
In 2016 and 2017, small charities had a higher share than medium and large charities in numbers of social service charities. There were 731 small charities in 2016 and were slightly decreased to 709 in 2017. The number of large charities was decreased from 207 to 197 and the number of medium charities was decreased from 124 to 116 at the period of 2016 to 2017.
From 2016 to 2017, the dominant staff type in social service sector was changed from part-time to full-time. The number of casual staff was the smallest in 2016 and 2017 compared with other staff types. A slight decrease in number of casual employees was seen in the period of 2016 to 2017; from 8,258 to 8,222 respectively. There were changes in the number of part-time employees and the number of full-time employees as well. The number of part-time employees decreased from 15,220 to 14,570 and the number of full-time employees increased from 11,818 to 20,905.
Civic and advocacy activites had the most volunteers in 2016 but had a huge drop from 77403 to 620 in 2017. In 2017, other education sectors got the highest numbers of volunteers. Social services were in the second place in both years and had a slightly decreasing from 75,605 to 70,289 people.
Small charities of social service sectors rely the most on volunteers followed by medium and large charities.
Volunteers accounted for 68.2% in the social service workforce in 2016 and was changed to 61.7% in 2017.
The below plots look at the number of charities running a deficit, surplus, or breaking even in each year. To count as having broken even, the net deficit/surplus for the charity has to equal exactly $0.
There were 539 charities in 2016 had net surplus, 406 charities had net debt and 117 charities that had net even.
In 2017, the number of charities that had net surplus was decreased to 537, 403 charities had net debt and only 82 charities obteined net even.
Numbers of charities that had net surplus were bigger than those that had net debt or even. There were the most charities that had net even in social service sector.
Please see the Australian Business Register website for more information on identifying invalid ABNs.
# Removing Invalid ABNs
## Reference Keybreaker file
ABN_Keybreaker <- read_excel("VCOSS Data/ABN Keybreaker.xlsx")
kable(ABN_Keybreaker,
caption = "ABN Format Guide") %>%
kable_styling(bootstrap_options = c("striped"),
full_width = FALSE)| Postion | Weighting |
|---|---|
| 1 | 10 |
| 2 | 1 |
| 3 | 3 |
| 4 | 5 |
| 5 | 7 |
| 6 | 9 |
| 7 | 11 |
| 8 | 13 |
| 9 | 15 |
| 10 | 17 |
| 11 | 19 |
## Function for checking ABNs
ABN_Checker <- function(ABN_No) {
sumproduct <- c()
for(position in 1:nchar(ABN_No)) {
number <- as.numeric(substr(ABN_No, position, position))
if(position == 1) {
number <- as.numeric(number - 1)
}
product <- (number * ABN_Keybreaker$Weighting[position])
sumproduct <- sum(sumproduct, product)
}
if(sumproduct %% 89 == 0) {
Check <- "Valid ABN"
} else {
Check <- "Invalid ABN"
}
return(Check)
}
ABN_Validator <- function(ABN_vector) {
sapply(ABN_vector, ABN_Checker)
}
VCOSS_ACNC_16 <- mutate(VCOSS_ACNC_16,
ABN_Validation = ABN_Validator(abn))
Invalid_ABNs_16 <- filter(VCOSS_ACNC_16,
ABN_Validation == "Invalid ABN")
VCOSS_ACNC_17 <- mutate(VCOSS_ACNC_17,
ABN_Validation = ABN_Validator(abn))
Invalid_ABNs_17 <- filter(VCOSS_ACNC_17,
ABN_Validation == "Invalid ABN")
## Filter out invalid ABNs from VCOSS dataframes
VCOSS_ACNC_16 <- VCOSS_ACNC_16[which(!(VCOSS_ACNC_16$abn %in% Invalid_ABNs_16$abn)), ]
VCOSS_ACNC_17 <- VCOSS_ACNC_17[which(!(VCOSS_ACNC_17$abn %in% Invalid_ABNs_17$abn)), ]
Social Services and all other Sectors